Detecting DTI Using Graph Embedding and Multi-Head Attention Mechanism

Drug-target interaction (DTI) prediction has an important role in drug discovery, significantly expediting the drug design and development process. This paper proposes a new model, ERW_BiAN, that seamlessly integrates graph embedding representations with deep learning network. Specifically, we utilize an improved graph embedding model, ERW, to generate comprehensive feature vectors for each node within the knowledge graph. Subsequently, these feature vectors are fed into the BiAN model, which leverages an attention mechanism to assign precise weights to sequences. Experimental results demonstrate the superior accuracy and predictive prowess of ERW_BiAN in DTI prediction tasks. Furthermore, the case about COVID-19 shows that ERW_BiAN has the better application potential for predicting drug-target interactions.